Search Results (1,306)

Accurate mapping of wetland vegetation is essential for ecosystem monitoring and conservation planning. Traditional workflows combining Sentinel-1 SAR, Sentinel-2 optical imagery, and topographic data have advanced vegetation classification but require extensive preprocessing and often yield fragmented boundaries and “salt-and-pepper” noise. In this study, we compare a conventional multi-sensor classification framework with a novel embedding-based approach derived from the AlphaEarth foundation model, using a cluster-guided Random Forest classifier applied to the dynamic wetland system of Narran Lake, New South Wales. Both approaches achieved high accuracy ac with test performance typically in the ranges: OA = 0.985–0.991, Cohen’s κ = 0.977–0.990, weighted F1 = 0.986–0.991, and MCC = 0.977–0.990. Embedding based maps showed markedly improved spatial coherence (lower edge density, local entropy, and patch fragmentation), producing smoother, ecologically consistent boundaries while requiring minimal preprocessing. Differences in class delineation were most evident in fire-affected and agricultural areas, where embeddings demonstrated greater resilience to spectral disturbance and post-fire variability. Although overall accuracies exceeded 0.98, these high values reflect the use of spectrally pure, homogeneous training samples rather than overfitting. The results highlight that embedding-driven methods can deliver cleaner, more interpretable vegetation maps with far less data preparation, underscoring their potential to streamline large-scale ecological monitoring and enhance the spatial realism of wetland mapping. Full article

As a natural flavonoid, the flavonoid luteolin is characterized by its powerful antioxidant and anti-inflammatory effects. While its precise mechanisms require further elucidation, existing evidence confirms its efficacy in ameliorating myocardial ischemia–reperfusion injury (MIRI). This research was designed to investigate the mechanism through which luteolin protects against MIRI. We established MIRI rat models through the ligation of left anterior descending coronary artery (LAD). To evaluate the cardioprotective effects of luteolin, echocardiographic analysis was performed, Hematoxylin and Eosin (HE) staining, and serum cardiac injury markers creatine kinase-MB (CK-MB) and lactate dehydrogenase (LDH). Cardiac vascular permeability was determined using Evans blue staining. To mimic ischemia–reperfusion injury, endothelial cells (ECs) were subjected to oxygen-glucose deprivation/reoxygenation (OGD/R) in vitro. Endothelial cell barrier function was evaluated through F-actin phalloidin staining and FITC-Dextran fluorescence leakage experiments. To elucidate the molecular mechanism, FOXP1 small interfering RNA (siRNA) and NLRP3 inhibitor MCC950 were administered. In MIRI rats, luteolin significantly improved cardiac function and preserved endothelial barrier integrity. These effects were associated with upregulation of FOXP1 and suppression of NOD-like receptor family pyrin domain containing 3 (NLRP3) inflammasome. In OGD/R-treated endothelial cells, luteolin restored barrier function and cell viability. The protective effects of luteolin were abolished after FOXP1 silencing. Pharmacological NLRP3 inhibition (MCC950) mirrored luteolin’s protection. Our study indicates that luteolin enhances endothelial barrier function and attenuates MIRI via the FOXP1-NLRP3 pathway. The current study provides a potential drug for MIRI treatment. Full article

Monitoring mechanical vibration is crucial for ensuring the structural integrity and optimal performance of unmanned aerial vehicles (UAVs). This study introduces a portable and low-cost system that enables integrated acquisition and analysis of UAV vibration data in a single step, using a Raspberry Pi 4B, data acquisition (DAQ) through a MCC128 DAQ HAT card, and six accelerometers positioned at strategic structural points. Ground-based engine tests at 2700 RPM allowed vibration data to be recorded under conditions similar to those of real operation. Data was processed with a Kalman filter, a Hann window function application, and frequency analysis via Fast Fourier Transform (FFT). The first and second wing bending natural frequencies were identified at 12.3 Hz and 17.5 Hz, respectively, as well as a significant component around 23 Hz, which is a subharmonic of the propulsion system excitation frequency near 45 Hz. The results indicate that the highest vibration amplitudes are concentrated at the wingtips and near the engine. The proposed system offers an accessible and flexible alternative to commercial equipment, integrating acquisition, processing, and real-time visualization. Moreover, its implementation facilitates the early detection of structural anomalies and improves the reliability and safety of UAVs. Full article

Support Vector Machines (SVMs) are widely used in critical decision-making applications, such as precision agriculture, due to their strong theoretical foundations and their ability to construct an optimal separating hyperplane in high-dimensional spaces. However, the effectiveness of SVMs is highly dependent on the efficiency of the optimization algorithm used to solve their underlying dual problem, which is often complex and constrained. Classical solvers, such as Sequential Minimal Optimization (SMO) and Stochastic Gradient Descent (SGD), present inherent limitations: SMO ensures numerical stability but lacks scalability and is sensitive to heuristics, while SGD scales well but suffers from unstable convergence and limited suitability for nonlinear kernels. To address these challenges, this study proposes a novel hybrid optimization framework based on Open Competency Optimization and Particle Swarm Optimization (OCO–PSO) to enhance the training of SVMs. The proposed approach combines the global exploration capability of PSO with the adaptive competency-based learning mechanism of OCO, enabling efficient exploration of the solution space, avoidance of local minima, and strict enforcement of dual constraints on the Lagrange multipliers. Across multiple datasets spanning medical (diabetes), agricultural yield, signal processing (sonar and ionosphere), and imbalanced synthetic data, the proposed OCO-PSO–SVM consistently outperforms classical SVM solvers (SMO and SGD) as well as widely used classifiers, including decision trees and random forests, in terms of accuracy, macro-F1-score, Matthews correlation coefficient (MCC), and ROC-AUC. On the Ionosphere dataset, OCO-PSO achieves an accuracy of $95.71\%$, an F1-score of $0.954$, and an MCC of $0.908$, matching the accuracy of random forest while offering superior interpretability through its kernel-based structure. In addition, the proposed method yields a sparser model with only 66 support vectors compared to 71 for standard SVC (a reduction of approximately $7\%$), while strictly satisfying the dual constraints with a near-zero violation of $1.3\times {10}^{-3}$. Notably, the optimal hyperparameters identified by OCO-PSO ($\mathrm{C}=2$, $\gamma \approx 0.062$) differ substantially from those obtained via Bayesian optimization for SVC ($\mathrm{C}=10$, $\gamma \approx 0.012$), indicating that the proposed approach explores alternative yet equally effective regions of the hypothesis space. The statistical significance and robustness of these improvements are confirmed through extensive validation using 1000 bootstrap replications, paired Student’s t-tests, Wilcoxon signed-rank tests, and Holm–Bonferroni correction. These results demonstrate that the proposed metaheuristic hybrid optimization framework constitutes a reliable, interpretable, and scalable alternative for training SVMs in complex and high-dimensional classification tasks. Full article

The closure of rural maternity units in hospitals across the United States contributes to health inequities; however, little is known about the effects of reopening maternity services in this context. We conducted a mixed-methods study to characterize labor and delivery outcomes and patient experiences associated with the reopening of a rural Level 1 Maternity Care Center (MCC) at a critical access hospital. We compared clinical outcomes and distance to care for patients who gave birth at the rural MCC in the three years after its opening with outcomes from a similar low-risk and geographically located sample who gave birth at a large suburban academic medical center in the same hospital system in the three years before the MCC reopened. We also conducted in-depth interviews with patients who gave birth at the MCC. Labor and delivery outcomes were similar across both groups, with significantly more care provided by family physicians and midwives and lower neonatal intensive care unit use at the MCC. The opening of the MCC halved the distance patients traveled to give birth, and patients reported high rates of satisfaction. Rural maternity care centers can improve access to quality care closer to home using a resource-appropriate model. Full article

This study re-evaluates ROC-AUC for binary classification under severe class imbalance (<3% positives). Despite its widespread use, ROC-AUC can mask operationally salient differences among classifiers when the costs of false positives and false negatives are asymmetric. Using three benchmarks, credit-card fraud detection (0.17%), yeast protein localization (1.35%), and ozone level detection (2.9%), we compare ROC-AUC with Matthews Correlation Coefficient, F2-score, H-measure, and PR-AUC. Our empirical analyses span 20 classifier–sampler configurations per dataset, combined with four classifiers (Logistic Regression, Random Forest, XGBoost, and CatBoost) and four oversampling methods plus a no-resampling baseline (no resampling, SMOTE, Borderline-SMOTE, SVM-SMOTE, ADASYN). ROC-AUC exhibits pronounced ceiling effects, yielding high scores even for underperforming models. In contrast, MCC and F2 align more closely with deployment-relevant costs and achieve the highest Kendall’s τ rank concordance across datasets; PR-AUC provides threshold-independent ranking, and H-measure integrates cost sensitivity. We quantify uncertainty and differences using stratified bootstrap confidence intervals, DeLong’s test for ROC-AUC, and Friedman–Nemenyi critical-difference diagrams, which collectively underscore the limited discriminative value of ROC-AUC in rare-event settings. The findings recommend a shift to a multi-metric evaluation framework: ROC-AUC should not be used as the primary metric in ultra-imbalanced settings; instead, MCC and F₂ are recommended as primary indicators, supplemented by PR-AUC and H-measure where ranking granularity and principled cost integration are required. This evidence encourages researchers and practitioners to move beyond sole reliance on ROC-AUC when evaluating classifiers in highly imbalanced data. Full article

We use 29 years of altimeter-derived sea level anomalies and geostrophic velocities (1993–2021) from the Copernicus Marine Service to identify the Mexican Coastal Current (MCC) and to examine how it interacts with the coastline. Variance-ellipse and empirical orthogonal function analyses isolate a narrow alongshore jet with a mean width of about 95 km and average speeds near 0.3 m ${\mathrm{s}}^{-1}$ that reverses direction semiannually: poleward in June and July and equatorward in the rest of the year. When the MCC impinges on broad concavities in the coast, the boundary layer separates, forming recirculation cells that intensify and detach as coherent eddies. These near-shore eddies have similar radii (from ∼30 km) and relative vorticity of $\pm 0.5\times {10}^{-5}$${\mathrm{s}}^{-1}$ at the beginning of their generation, and they propagate offshore once the current weakens. A simple numerical model reproduces the observed behavior and suggests that eddy formation is controlled by flow separation rather than generic instability. The semiannual change in direction of the MCC indicate a link with the larger-scale North Equatorial Countercurrent and Costa Rica Coastal Current systems of the eastern tropical Pacific. Full article

Time delays are intrinsic to mitotic regulation, particularly within the spindle assembly checkpoint (SAC) and the spindle position checkpoint (SPOC). These delays emerge from multi-step protein activation, molecular transport, force-dependent conformational transitions, and spatial redistribution of regulatory complexes. They span seconds to minutes and strongly influence checkpoint activation, maintenance, and silencing. Increasing evidence shows that such delayed processes shape mitotic timing, checkpoint robustness, and cell-fate decisions. While classical ordinary differential equation (ODE) models assume instantaneous biochemical responses, delay differential equations (DDEs) provide a natural framework for representing these finite timescales by explicitly incorporating system history. Recent DDE-based studies have revealed how delayed signaling contributes to bistability, oscillatory responses, prolonged mitotic arrest, and variability in checkpoint outputs. This review summarizes the biological origins of delays in SAC and SPOC, including Mad2 activation, MCC assembly and turnover, APC/C reactivation, tension maturation at kinetochores, and Bfa1–Bub2 regulation of Tem1. The article further discusses how mechanistic models with explicit delays improve our understanding of SAC–SPOC ordering, error-correction dynamics, and mitotic exit control. Finally, open challenges and future directions are outlined for integrative delay-aware modeling that unifies biochemical, mechanical, and spatial processes to better explain checkpoint function and chromosomal stability. Full article

Oral cancer (OC) remains a major global health concern with survival often limited by late diagnosis. Early and accurate detection is essential to improve patient outcomes and guide treatment decisions. In this study we propose a computer aided diagnostic (CAD) framework for classifying oral squamous cell carcinoma from histopathology images. The model combines Swin transformer for hierarchical feature extraction with vision transformer (ViT) to capture long range dependencies across image regions. SHapley Additive exPlanations (SHAP) based feature selection enhances interpretability by highlighting the most informative features while preprocessing steps such as stain normalization and contrast enhancement improve model generalization and reduce sample variability. Evaluated on a publicly available dataset the framework achieved 99.25% accuracy (ACC) 99.21% sensitivity and a matthews correlation coefficient (MCC) of 98.21% outperforming existing methods. Ablation studies highlighted the importance of positional encoding and statistical analyses confirmed the robustness and reliability of results. To support real-time inference and scalable deployment the proposed model has been integrated into a FastAPI-based web application. This framework offers a powerful interpretable and practical tool for early OC detection and has potential for integration into routine clinical workflows. Full article

The Loess Plateau is highly susceptible to landslides due to its fragile geological structure and frequent human activities, particularly old landslides with historical structural damage. The features of these landslides in remote sensing images become blurred over time, leading to huge challenges in detection. Considering that old landslides exhibit obvious shape characteristics, we propose ResU-SPMNet, a deep learning model that integrates shape characteristics into the baseline ResU-Net. The proposed model consists of three components: ResU-Net, shape prior module (SPM), and the atrous spatial pyramid pooling (ASPP) module, which jointly enhance segmentation performance from the perspectives of shape constraints and multi-scale feature representation. To validate the effectiveness of the proposed approach, old landslides in representative regions of the Loess Plateau were selected as the study targets. Results show that the proposed model outperforms ResU-Net, SegNet, MultiResUnet, and DeepLabv3+ in old landslide segmentation, achieving an F1-score of 0.6669 and an MCC of 0.6167. Moreover, generalization tests conducted in independent regions indicate that the model exhibits strong robustness across different seasons. The best performance is achieved in summer, whereas performance declines in winter due to adverse factors such as reduced illumination and snow or ice cover. Full article

Background: Accurate recognition of promoter sequences in Escherichia coli is fundamental for understanding gene regulation and engineering synthetic biological systems. However, existing computational methods struggle to simultaneously model long-range genomic dependencies and fine-grained local motifs, particularly the degenerate −10 and −35 elements of $\sigma 70$ promoters. To address this gap, we propose DNABERT2-CAMP, a novel hybrid deep learning framework designed to integrate global contextual understanding with high-resolution local motif detection for robust promoter identification. Methods: We constructed a balanced dataset of 8720 experimentally validated and negative 81-bp sequences from RegulonDB, literature, and the E. coli K-12 genome. Our model combines a pre-trained DNABERT-2 Transformer for global sequence encoding with a custom CAMP module (CNN-Attention-Mean Pooling) for local feature refinement. We evaluated performance using 5-fold cross-validation and an independent external test set, reporting standard metrics including accuracy, ROC AUC, and Matthews correlation coefficient (MCC). Results: DNABERT2-CAMP achieved 93.10% accuracy and 97.28% ROC AUC in cross-validation, outperforming existing methods including DNABERT. On an independent test set, it maintained strong generalization (89.83% accuracy, 92.79% ROC AUC). Interpretability analyses confirmed biologically plausible attention over canonical promoter regions and CNN-identified AT-rich/-35-like motifs. Conclusions: DNABERT2-CAMP demonstrates that synergistically combining pre-trained Transformers with convolutional motif detection significantly improves promoter recognition accuracy and interpretability. This framework offers a powerful, generalizable tool for genomic annotation and synthetic biology applications. Full article

At present, composite phase change materials are widely studied for battery thermal management. However, to ensure the battery’s thermal safety, it is necessary not only to control the temperature during regular operation, but also to prevent sudden thermal runaway. This basic function depends on the flame-retardant properties of the composite phase change materials. In this study, a hexagonal double-layer hollow nanocage S2 with defect orientation was prepared and combined with carbon nanotubes (PNT) derived from polypyrrole (PPy) tubes to form a high adsorption mixture. Multifunctional composite phase change material PNT/S2@PEG/TEP was prepared by adsorbing and coating polyethylene glycol 8000 (PEG-8000) and triethyl phosphate (TEP) with microfibrillated cellulose nanofibers (CNF) as the skeleton. The characterization shows that its thermal conductivity is 0.65 W/m·K and its phase transition enthalpy is 146.1 J/g, demonstrating its excellent thermal regulation. Microcalorimetric testing (MCC) confirmed its flame-retardant ability, attributed to the strong adsorption of PNT/S2 on PEG-8000 and TEP, the improvement in PNT’s thermal conductivity, and the contribution of CNF to flexibility. This composite phase change material, with excellent comprehensive properties, has broad application prospects in thermal safety for electronic equipment, significantly expanding its practical scope. Full article

MicroRNAs (miRNAs) have emerged as critical post-transcriptional regulators in melanoma and non-melanoma skin cancers (NMSCs), yet their full biological and clinical significance remains incompletely defined. This review synthesizes current evidence on miRNA dysregulation across basal cell carcinoma (BCC), cutaneous squamous cell carcinoma (cSCC), Merkel cell carcinoma (MCC), and melanoma, emphasizing their diagnostic, prognostic, and therapeutic relevance. In BCC, distinct miRNA expression signatures differentiate tumor tissue from normal skin and correlate with histopathological subtypes. miR-383-5p, miR-4705, miR-145-5p, and miR-18a show strong diagnostic potential, while downregulation of miR-34a is consistently associated with greater tumor aggressiveness. Subtype-specific profiles further delineate superficial versus infiltrative lesions, highlighting miRNAs as markers of tumor behavior. cSCC similarly demonstrates characteristic miRNA alterations. miR-31 is markedly upregulated during the transition from actinic keratosis to invasive carcinoma, whereas high miR-205 and low miR-203 levels correlate with poor and favorable prognosis, respectively. Regarding MCC, many miRNAs such as miR-375 and miR-182 may present a clinical value for potential biomarkers, as they are upregulated in MCC. Merkel cell carcinoma has also been linked with Merkel cell polyomavirus (MCPyV). Melanoma exhibits a complex miRNA landscape, including oncogenic miR-18a-5p and miR-146a, and tumor-suppressive miR-128-3p. Several miRNAs correlate with metastatic potential, BRAF mutation status, and therapeutic resistance, particularly miR-181a/b, underscoring their potential as predictive biomarkers. Overall, current evidence supports miRNAs as promising diagnostic, prognostic, and predictive biomarkers in cutaneous oncology. Standardized methodologies and large-scale validation remain essential for their integration into routine clinical practice. Full article

Speech is one of the earliest-affected modalities in Parkinson’s disease (PD). For more reliable PD evaluation, speech-based telediagnosis studies often use multiple samples from the same subject to capture variability in speech recordings. However, many existing studies split samples—rather than subjects—between training and testing, creating a biased experimental setup that allows data (samples) from the same subject to appear in both sets. This raises concerns for reliable PD evaluation due to data leakage, which results in over-optimistic performance (often close to 100%). In addition, detecting subtle vocal impairments from speech recordings using multiple feature extraction techniques often increases data dimensionality, although only some features are discriminative while others are redundant or non-informative. To address this and build a reliable speech-based PD telediagnosis system, the key contributions of this work are two-fold: (1) a uniform (fair) experimental setup employing subject-wise symmetric (stratified) splitting in 5-fold cross-validation to ensure better generalization in PD prediction, and (2) a novel hybrid data complexity-aware (HDC) feature selection method that improves class separability. This work further contributes to the research community by releasing a publicly accessible five-fold benchmark version of the Parkinson’s speech dataset for consistent and reproducible evaluation. The proposed HDC method analyzes multiple aspects of class separability to select discriminative features, resulting in reduced data complexity in the feature space. In particular, it uses data complexity measures (F4, F1, F3) to assess minimal feature overlap and ReliefF to evaluate the separation of borderline points. Experimental results show that the top-50 discriminative features selected by the proposed HDC outperform existing feature selection algorithms on five out of six classifiers, achieving the highest performance with 0.86 accuracy, 0.70 G-mean, 0.91 F1-score, and 0.58 MCC using an SVM (RBF) classifier. Full article

Background/Objectives: Immunohistochemistry (IHC) is a critical diagnostic tool in forensic pathology, enabling molecular-level assessment of wound vitality, post-mortem interval, and cause of death. However, IHC interpretation is subject to variability due to its reliance on human expertise. This study investigates whether artificial intelligence (AI), specifically a generative model, can assist in the diagnostic evaluation of IHC slides and replicate expert-level scoring, thereby improving consistency and reproducibility. Methods: A total of 225 high-resolution IHC images were classified into five immunoreactivity categories. The AI model (ChatGPT-4V) was trained on 150 labeled images and tested blindly on 75 unseen slides. Performance was assessed using confusion matrices, per-class precision/recall/F1, overall accuracy, Cohen’s κ (unweighted and weighted), and binary metrics (sensitivity, specificity, MCC). Results: Overall accuracy was 81.3% (95% CI: 71.1–88.5%), with substantial agreement (κ = 0.767 unweighted; 0.805 linear-weighted; 0.848 quadratic-weighted). Binary classification achieved a sensitivity of 98.3%, specificity of 93.3%, MCC of 0.92. Accuracy was highest in extreme categories (− and +++, 93.3%), while intermediate classes (+ and ++) showed reduced performance (error rates up to 33%). Evaluation was rapid and consistent but lacked interpretative reasoning and struggled with borderline cases. Conclusions: AI-assisted diagnostic evaluation of IHC slides demonstrates promising accuracy and consistency, particularly in well-defined staining patterns. While not a replacement for human expertise, AI can serve as a valuable adjunct in forensic pathology, supporting rapid and standardized assessments. Ethical and legal considerations must guide its implementation in medico-legal contexts. Full article